CTmax Data
temp_lat_plot = ctmax_data %>%
select(lat, collection_temp) %>%
distinct() %>%
ggplot(aes(x = lat, y = collection_temp)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(size = 3) +
labs(x = "Latitude",
y = "Collection Temp. (°C)") +
theme_matt() +
theme(legend.position = "right")
ctmax_temp_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species),
size = 3) +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ctmax_lat_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = lat, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species),
size = 3) +
labs(x = "Latitude",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ctmax_elev_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = elevation, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species),
size = 3) +
labs(x = "Elevation (m)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ggpubr::ggarrange(temp_lat_plot, ctmax_temp_plot, ctmax_lat_plot, ctmax_elev_plot, common.legend = T, legend = "right", nrow = 2, ncol = 2, labels = "AUTO")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = ctmax, colour = species)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_color_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
filter(str_detect(species, pattern = "skisto") |
str_detect(species, pattern = "lepto") |
str_detect(species, pattern = "aglao")) %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
group_by(collection_date, species, collection_temp) %>%
summarise(mean_ctmax = mean(ctmax),
ctmax_sd = sd(ctmax),
ctmax_n = n(),
ctmax_se = ctmax_sd / sqrt(ctmax_n)) %>%
ggplot(aes(x = collection_temp, y = mean_ctmax, colour = species)) +
geom_smooth(method = "lm", se=F, linewidth = 2) +
geom_point(size = 2) +
geom_errorbar(aes(ymin = mean_ctmax - ctmax_se,
ymax = mean_ctmax + ctmax_se),
width = 0.3, linewidth = 1) +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = size, colour = species)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "Prosome Length (mm)") +
scale_color_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = egg_volume, colour = species)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "Egg Volume (mm^3)") +
scale_color_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
select(elevation, collection_temp) %>%
distinct() %>%
ggplot(aes(x = elevation, y = collection_temp)) +
geom_point(size = 3) +
labs(x = "Elevation (m)",
y = "Collection Temp. (°C)") +
theme_matt()

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = size, y = ctmax, colour = species)) +
#facet_wrap(.~species) +
geom_point(size = 1) +
theme_matt() +
labs(x = "Length (mm)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme(legend.position = "none")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = size, y = fecundity, colour = species)) +
facet_wrap(.~species) +
geom_point(size = 1) +
theme_matt() +
labs(x = "Length (mm)",
y = "Fecundity (# eggs)") +
scale_colour_manual(values = skisto_cols) +
theme(legend.position = "none")

ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) +
geom_smooth(method = "lm", formula = y ~ exp(x)) +
geom_point()+
labs(x = "Prosome Length (mm)",
y = "Total Egg Volume (mm^3)") +
theme_matt()

Data for just Skistodiaptomus pallidus is shown below. Point
color is arranged according to latitude.
ctmax_data %>%
filter(species == "skistodiaptomus_pallidus") %>%
mutate(site = fct_reorder(site, lat, .desc = T)) %>%
# group_by(site) %>%
# summarise(size = mean(size, na.rm = T),
# total_egg_volume = mean(total_egg_volume, na.rm = T)) %>%
ggplot(aes(x = size, y = total_egg_volume)) +
geom_smooth(method = "lm", formula = y ~ exp(x),
colour = "black") +
geom_point(aes(colour = site))+
scale_color_viridis_d(direction = 1,
option = "D") +
labs(x = "Prosome Length (mm)",
y = "Total Egg Volume (mm^3)") +
theme_matt() +
theme(legend.position = "right")

model_data = ctmax_data %>%
mutate("genus" = str_split_fixed(species, pattern = "_", n = 2)[,1],
genus = tools::toTitleCase(genus),
"doy" = yday(collection_date)) %>%
select(site, collection_date, doy, collection_temp, lat, elevation, species, genus, sample_id, fecundity, total_egg_volume, size, ctmax) %>%
filter(genus != "MH") %>%
mutate(total_egg_volume = if_else(is.na(total_egg_volume), 0, total_egg_volume),
collection_temp_sc = scale(collection_temp),
lat_sc = scale(lat),
elevation_sc = scale(elevation),
tev_sc = scale(total_egg_volume))
ctmax_overall.model = lm(data = model_data,
ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume)
drop1(ctmax_overall.model, test = "F")
## Single term deletions
##
## Model:
## ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 485.97 -4.365
## genus 2 138.079 624.04 117.677 70.6070 < 2.2e-16 ***
## collection_temp 1 105.341 591.31 92.519 107.7332 < 2.2e-16 ***
## lat 1 113.236 599.20 99.203 115.8076 < 2.2e-16 ***
## elevation 1 6.509 492.48 0.341 6.6572 0.01016 *
## total_egg_volume 1 21.484 507.45 15.438 21.9722 3.578e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#MuMIn::dredge(ctmax_temp.model)
car::Anova(ctmax_overall.model)
## Anova Table (Type II tests)
##
## Response: ctmax
## Sum Sq Df F value Pr(>F)
## genus 138.08 2 70.6070 < 2.2e-16 ***
## collection_temp 105.34 1 107.7332 < 2.2e-16 ***
## lat 113.24 1 115.8076 < 2.2e-16 ***
## elevation 6.51 1 6.6572 0.01016 *
## total_egg_volume 21.48 1 21.9722 3.578e-06 ***
## Residuals 485.97 497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::check_model(ctmax_overall.model)

egg.model = lm(data = model_data,
ctmax ~ collection_temp + total_egg_volume + size)
performance::check_model(egg.model)

effectsize::effectsize(egg.model)
## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------
## (Intercept) | 6.39e-16 | [-0.07, 0.07]
## collection temp | 0.61 | [ 0.54, 0.68]
## total egg volume | 0.15 | [ 0.08, 0.23]
## size | 0.37 | [ 0.30, 0.44]
emmeans::emmeans(ctmax_overall.model, specs = "genus") %>%
data.frame() %>%
mutate(genus = fct_reorder(genus, .x = emmean, .desc = T)) %>%
ggplot(aes(genus, y = emmean)) +
geom_point(size = 4) +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE),
width = 0.2, linewidth = 1) +
labs(x = "") +
theme_matt() +
theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))

ctmax_temp.model = lm(data = model_data,
ctmax ~ species + collection_temp)
drop1(ctmax_temp.model,
scope = ~.,
test = "F")
## Single term deletions
##
## Model:
## ctmax ~ species + collection_temp
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 328.04 -196.438
## species 8 587.82 915.86 305.034 110.65 < 2.2e-16 ***
## collection_temp 1 116.01 444.05 -45.823 174.70 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::check_model(ctmax_temp.model)

sp_means = emmeans::emmeans(ctmax_temp.model, "species") %>%
data.frame() %>%
drop_na() %>%
select(species, "ctmax" = emmean, lower.CL, upper.CL)
sp_means %>%
mutate(species = str_replace(species, pattern = "_", replacement = " "),
species = str_to_sentence(species),
species = fct_reorder(species, .x = ctmax)) %>%
ggplot(aes(x = species, y = ctmax, colour = species)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL),
width = 0.5) +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(axis.text = element_text(angle = 300, hjust = 0, vjust = 0.5),
legend.position = "none")

ctmax_data %>%
mutate(group_id = paste(site, species, collection_date)) %>%
ggplot(aes(x = fecundity, y = site, fill = site)) +
geom_density_ridges(bandwidth = 2,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "D", direction = -1) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(group_id = paste(site, species, collection_date)) %>%
ggplot(aes(x = size, y = site, fill = site, group = group_id)) +
geom_density_ridges(bandwidth = 0.02,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "D", direction = -1) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(group_id = paste(site, species, collection_date)) %>%
ggplot(aes(x = ctmax, y = site, fill = site, group = group_id)) +
geom_density_ridges(bandwidth = 0.3,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "D", direction = -1) +
labs(x = "CTmax (°C)") +
theme_matt() +
theme(legend.position = "none")

F3 Data
Skistodiaptomus pallidus was collected from three sites
(Centennial Park - CO, Ochsner Pond - OH, and Center Springs Pond - CT)
were reared in the lab at 16°C for at least three generations. CTmax was
measured for these copepods to test for genetic variation in thermal
limits in this widely distributed species.
Lab reared copepods varied in size, with Centennial Park individuals
~0.1 mm longer than those from Ochsner Pond.
f3_data %>%
ggplot(aes(x = site, y = size)) +
geom_boxplot() +
geom_point(position = position_jitter(height = 0, width = 0.1)) +
labs(x = "Site",
y = "Prosome Length (mm)") +
theme_matt()

f3_size.model = lme4::lmer(data = f3_data,
size ~ site + (1 | experiment_date))
#performance::check_model(f3_size.model)
car::Anova(f3_size.model)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: size
## Chisq Df Pr(>Chisq)
## site 25.398 1 4.664e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Upper thermal limit did not vary between the populations.
f3_data %>%
ggplot(aes(x = site, y = ctmax)) +
geom_boxplot() +
geom_point(position = position_jitter(height = 0, width = 0.1)) +
labs(x = "Site",
y = "CTmax (°C)") +
theme_matt()

f3_ctmax.model = lme4::lmer(data = f3_data,
ctmax ~ site + (1|experiment_date) + (1|tube))
#performance::check_model(f3_ctmax.model)
car::Anova(f3_ctmax.model)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ctmax
## Chisq Df Pr(>Chisq)
## site 0.827 1 0.3631
f3_data %>%
group_by(experiment_date, site) %>%
summarise(mean_ctmax = mean(ctmax),
se_ctmax = sd(ctmax) / sqrt(n())) %>%
ggplot(aes(experiment_date, y = mean_ctmax, colour = site, group = site)) +
geom_point(data = f3_data,
aes(x = experiment_date, y = ctmax, colour = site),
size = 1, alpha = 0.3,
position = position_jitterdodge(jitter.height = 0, jitter.width = 0.05,
dodge.width = 0.3)) +
geom_line(linewidth = 1.3,
position = position_dodge(width = 0.3)) +
geom_errorbar(aes(ymin = mean_ctmax - se_ctmax, ymax = mean_ctmax + se_ctmax),
position = position_dodge(width = 0.3),
width = 0.25, linewidth = 1) +
geom_point(size = 3,
position = position_dodge(width = 0.3)) +
labs(x = "Experiment Date",
y = "CTmax (°C)") +
theme_matt() +
theme(legend.position = "right")

Fecundity also appears to vary between populations, even after
rearing in lab for several generations, although this difference does
not appear to be significant.
ggplot(f3_data, aes(x = site, y = fecundity)) +
geom_boxplot() +
geom_point(position = position_jitter(height = 0, width = 0.1)) +
labs(x = "Site",
y = "Clutch Size (eggs per female)") +
theme_matt()

# f3_fecund.model = glm(data = f3_data,
# fecundity ~ site,
# family="poisson")
f3_fecund.model = lme4::glmer(data = f3_data,
fecundity ~ site + (1|experiment_date),
family="poisson")
# performance::check_model(f3_fecund.model)
car::Anova(f3_fecund.model)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: fecundity
## Chisq Df Pr(>Chisq)
## site 2.1596 1 0.1417
We also examined how oxygen consumption rates varied across
temperatures for the two populations. Results are preliminary, but it
seems like the two populations may have similar optimum
temperatures.
tpc_rates %>%
filter(temp != 32) %>%
filter(!(temp > 15 & rsq < 0.7)) %>%
ggplot(aes(x = temp, y = rate, colour = treatment)) +
#facet_wrap(treatment~., nrow = 2) +
geom_point() +
geom_smooth() +
theme_bw()

To summarize the initial findings, Centennial Park copepods had
larger body sizes but smaller clutch sizes than copepods from Ochsner
Pond. CTmax and thermal optima were similar between the two
populations.
---
title: Diaptomid Thermal Limits
date: "`r Sys.Date()`"
output: 
  html_document:
          code_folding: hide
          code_download: true
          toc: true
          toc_float: true
  github_document:
          html_preview: false
          toc: true
          toc_depth: 3
---

```{r setup, include=T, message = F, warning = F, echo = F}
knitr::opts_chunk$set(
  echo = knitr::is_html_output(),
  fig.align = "center",
  fig.path = "../Figures/markdown/",
  dev = c("png", "pdf"),
  message = FALSE,
  warning = FALSE,
  collapse = T
)

theme_matt = function(base_size = 18,
                      dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  ggpubr::theme_pubr(base_family="sans") %+replace% 
    theme(
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

theme_matt_facets = function(base_size = 18,
                             dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  theme_bw(base_family="sans") %+replace% 
    theme(
      panel.grid = element_blank(),
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      strip.text.x = element_text(size = base_size),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}
```

## Site Map

```{r sampled-sites, fig.width=10, fig.height=6}
coords = ctmax_data %>%
  inner_join(site_data, by = c("site", "lat", "collection_temp")) %>% 
  dplyr::select(site, long, lat, collection_temp, elevation) %>%
  drop_na(collection_temp) %>% 
  distinct()

map_data("world") %>% 
  filter(region %in% c("USA", "Canada")) %>% 
  ggplot() + 
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = "grey92", colour = "grey40", linewidth = 0.1) + 
  coord_map(xlim = c(-110,-60),
            ylim = c(25, 55)) + 
  geom_point(data = coords,
             mapping = aes(x = long, y = lat),
             size = 2) +
  labs(x = "Longitude", 
       y = "Latitude",
       colour = "Elev. (m)") + 
  theme_matt() + 
  theme(legend.position = "right")
```

## CTmax Data 

```{r fig.width=14, fig.height=10}
temp_lat_plot = ctmax_data %>% 
  select(lat, collection_temp) %>% 
  distinct() %>% 
  ggplot(aes(x = lat, y = collection_temp)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(size = 3) + 
  labs(x = "Latitude", 
       y = "Collection Temp. (°C)") + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_temp_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_lat_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = lat, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) + 
  labs(x = "Latitude", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_elev_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = elevation, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) +
  labs(x = "Elevation (m)", 
       y = "CTmax (°C)") +
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggpubr::ggarrange(temp_lat_plot, ctmax_temp_plot, ctmax_lat_plot, ctmax_elev_plot, common.legend = T, legend = "right", nrow = 2, ncol = 2, labels = "AUTO")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r fig.width=10, fig.height=6}
ctmax_data %>% 
  filter(str_detect(species, pattern = "skisto") | 
           str_detect(species, pattern = "lepto") | 
           str_detect(species, pattern = "aglao")) %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  group_by(collection_date, species, collection_temp) %>% 
  summarise(mean_ctmax = mean(ctmax),
            ctmax_sd = sd(ctmax),
            ctmax_n = n(), 
            ctmax_se = ctmax_sd / sqrt(ctmax_n)) %>% 
  ggplot(aes(x = collection_temp, y = mean_ctmax, colour = species)) + 
  geom_smooth(method = "lm", se=F, linewidth = 2) + 
  geom_point(size = 2) + 
  geom_errorbar(aes(ymin = mean_ctmax - ctmax_se, 
                    ymax = mean_ctmax + ctmax_se),
                width = 0.3, linewidth = 1) + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = size, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Prosome Length (mm)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = egg_volume, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Egg Volume (mm^3)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r}
ctmax_data %>% 
  select(elevation, collection_temp) %>% 
  distinct() %>% 
  ggplot(aes(x = elevation, y = collection_temp)) + 
  geom_point(size = 3) +
  labs(x = "Elevation (m)", 
       y = "Collection Temp. (°C)") + 
  theme_matt()
```

```{r fig.width=8, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = size, y = ctmax, colour = species)) + 
  #facet_wrap(.~species) + 
  geom_point(size = 1) + 
  theme_matt() + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme(legend.position = "none")
```

```{r fig.width=8, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = size, y = fecundity, colour = species)) + 
  facet_wrap(.~species) + 
  geom_point(size = 1) + 
  theme_matt() + 
  labs(x = "Length (mm)", 
       y = "Fecundity (# eggs)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme(legend.position = "none")
```

```{r}
ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) + 
  geom_smooth(method = "lm", formula = y ~ exp(x)) + 
  geom_point()+
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt()

```

Data for just *Skistodiaptomus pallidus* is shown below. Point color is arranged according to latitude. 
```{r fig.width=8.5, fig.height=5.5}
ctmax_data %>% 
  filter(species == "skistodiaptomus_pallidus") %>%
  mutate(site = fct_reorder(site, lat, .desc = T)) %>% 
  # group_by(site) %>% 
  # summarise(size = mean(size, na.rm = T), 
  #          total_egg_volume = mean(total_egg_volume, na.rm = T)) %>% 
  ggplot(aes(x = size, y = total_egg_volume)) + 
  geom_smooth(method = "lm", formula = y ~ exp(x), 
              colour = "black") + 
  geom_point(aes(colour = site))+
  scale_color_viridis_d(direction = 1, 
                        option = "D") + 
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt() + 
  theme(legend.position = "right")
```


```{r}
model_data = ctmax_data %>% 
  mutate("genus" = str_split_fixed(species, pattern = "_", n = 2)[,1],
         genus = tools::toTitleCase(genus),
         "doy" = yday(collection_date)) %>% 
  select(site, collection_date, doy, collection_temp, lat, elevation, species, genus, sample_id, fecundity, total_egg_volume, size, ctmax) %>% 
  filter(genus != "MH") %>%  
  mutate(total_egg_volume = if_else(is.na(total_egg_volume), 0, total_egg_volume),
         collection_temp_sc = scale(collection_temp),
         lat_sc = scale(lat), 
         elevation_sc = scale(elevation),
         tev_sc = scale(total_egg_volume)) 

ctmax_overall.model = lm(data = model_data, 
                         ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume)

drop1(ctmax_overall.model, test = "F")

#MuMIn::dredge(ctmax_temp.model)

car::Anova(ctmax_overall.model)

performance::check_model(ctmax_overall.model)

egg.model = lm(data = model_data, 
                         ctmax ~ collection_temp + total_egg_volume + size)

performance::check_model(egg.model)

effectsize::effectsize(egg.model)

emmeans::emmeans(ctmax_overall.model, specs = "genus") %>% 
  data.frame() %>% 
  mutate(genus = fct_reorder(genus, .x = emmean, .desc = T)) %>% 
  ggplot(aes(genus, y = emmean)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), 
                width = 0.2, linewidth = 1) + 
  labs(x = "") + 
  theme_matt() + 
  theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))
```

```{r}

ctmax_temp.model = lm(data = model_data, 
                      ctmax ~ species + collection_temp)

drop1(ctmax_temp.model, 
      scope = ~.,
      test = "F")

performance::check_model(ctmax_temp.model)

sp_means = emmeans::emmeans(ctmax_temp.model, "species") %>% 
  data.frame() %>% 
  drop_na() %>%  
  select(species, "ctmax" = emmean, lower.CL, upper.CL)


sp_means %>% 
  mutate(species = str_replace(species, pattern = "_", replacement = " "),
         species = str_to_sentence(species),
         species = fct_reorder(species, .x = ctmax)) %>% 
ggplot(aes(x = species, y = ctmax, colour = species)) + 
  geom_point(size = 3) + 
  geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), 
                width = 0.5) + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(axis.text = element_text(angle = 300, hjust = 0, vjust = 0.5), 
        legend.position = "none")

```



```{r fecundity-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species, collection_date)) %>% 
  ggplot(aes(x = fecundity, y = site, fill = site)) + 
  geom_density_ridges(bandwidth = 2,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r size-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species, collection_date)) %>% 
  ggplot(aes(x = size, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.02,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r ctmax-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species, collection_date)) %>% 
  ggplot(aes(x = ctmax, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.3,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  labs(x = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "none")
```

## F3 Data 
*Skistodiaptomus pallidus* was collected from three sites (Centennial Park - CO, Ochsner Pond - OH, and Center Springs Pond - CT) were reared in the lab at 16°C for at least three generations. CTmax was measured for these copepods to test for genetic variation in thermal limits in this widely distributed species. 

Lab reared copepods varied in size, with Centennial Park individuals ~0.1 mm longer than those from Ochsner Pond. 

```{r}
f3_data %>%
  ggplot(aes(x = site, y = size)) + 
  geom_boxplot() + 
  geom_point(position = position_jitter(height = 0, width = 0.1)) + 
  labs(x = "Site", 
       y = "Prosome Length (mm)") + 
  theme_matt()
```

```{r}
f3_size.model = lme4::lmer(data = f3_data,
                      size ~ site + (1 | experiment_date))

#performance::check_model(f3_size.model)

car::Anova(f3_size.model)

```

Upper thermal limit did not vary between the populations. 

```{r}
f3_data %>%
ggplot(aes(x = site, y = ctmax)) + 
  geom_boxplot() + 
  geom_point(position = position_jitter(height = 0, width = 0.1)) + 
  labs(x = "Site", 
       y = "CTmax (°C)") + 
  theme_matt()
```

```{r}

f3_ctmax.model = lme4::lmer(data = f3_data, 
                      ctmax ~ site + (1|experiment_date) + (1|tube))

#performance::check_model(f3_ctmax.model)

car::Anova(f3_ctmax.model)

```

```{r}
f3_data %>% 
  group_by(experiment_date, site) %>% 
  summarise(mean_ctmax = mean(ctmax), 
            se_ctmax = sd(ctmax) / sqrt(n())) %>% 
ggplot(aes(experiment_date, y = mean_ctmax, colour = site, group = site)) + 
  geom_point(data = f3_data,
             aes(x = experiment_date, y = ctmax, colour = site),
             size = 1, alpha = 0.3,
             position = position_jitterdodge(jitter.height = 0, jitter.width = 0.05, 
                                             dodge.width = 0.3)) + 
    geom_line(linewidth = 1.3,
              position = position_dodge(width = 0.3)) + 
  geom_errorbar(aes(ymin = mean_ctmax - se_ctmax, ymax = mean_ctmax + se_ctmax),
                position = position_dodge(width = 0.3),
                width = 0.25, linewidth = 1) + 
    geom_point(size = 3,
               position = position_dodge(width = 0.3)) + 
  labs(x = "Experiment Date", 
       y = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "right")
```


Fecundity also appears to vary between populations, even after rearing in lab for several generations, although this difference does not appear to be significant. 

```{r}

ggplot(f3_data, aes(x = site, y = fecundity)) + 
  geom_boxplot() + 
  geom_point(position = position_jitter(height = 0, width = 0.1)) + 
  labs(x = "Site", 
       y = "Clutch Size (eggs per female)") + 
  theme_matt()

```

```{r}

# f3_fecund.model = glm(data = f3_data, 
#                       fecundity ~ site,
#                       family="poisson")

f3_fecund.model = lme4::glmer(data = f3_data, 
                      fecundity ~ site + (1|experiment_date),
                      family="poisson")

# performance::check_model(f3_fecund.model)

car::Anova(f3_fecund.model)
```

We also examined how oxygen consumption rates varied across temperatures for the two populations. Results are preliminary, but it seems like the two populations may have similar optimum temperatures. 

```{r}
tpc_rates %>% 
  filter(temp != 32) %>% 
  filter(!(temp > 15 & rsq < 0.7)) %>% 
  ggplot(aes(x = temp, y = rate, colour = treatment)) + 
  #facet_wrap(treatment~., nrow = 2) + 
  geom_point() + 
  geom_smooth() + 
  theme_bw()
```

To summarize the initial findings, Centennial Park copepods had larger body sizes but smaller clutch sizes than copepods from Ochsner Pond. CTmax and thermal optima were similar between the two populations. 

## High throughput size measurements 

```{r}
scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>% 
  ggplot(aes(x = length, fill = species)) + 
  facet_wrap(site~., nrow = 3) + 
  geom_histogram(binwidth = 0.01, colour = "grey10", linewidth = 0.25) + 
  scale_fill_manual(values = skisto_cols) + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        strip.text = element_text(face = "bold"),
        legend.position = "bottom")
```

```{r}

scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>%
  ggplot(aes(x = length, y = site, fill = species)) + 
  geom_density_ridges(bandwidth = 0.02,
                      jittered_points = TRUE,
                      position = position_points_jitter(yoffset = -0.15, width = 0, height = 0.1),
                      point_alpha = 0.3, point_colour = "grey30")  + 
  labs(y = "",
       x = "Prosome Length (mm)") + 
  scale_fill_manual(values = skisto_cols) + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        strip.text = element_text(face = "bold"),
        legend.position = "bottom")

```

```{r}
scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>%
  inner_join(site_data) %>% 
  ggplot(aes(x = collection_temp, y = length)) + 
  geom_point(position = position_jitter(width = 0.08, height = 0)) + 
  theme_matt()
```


## COI Barcoding 

```{r, fig.align="center"}
knitr::include_graphics("../Figures/species_prop_plot.png")
```

```{r, fig.align="center"}
knitr::include_graphics("../Figures/clade_prop_plot.png")
```

```{r, fig.align="center"}
knitr::include_graphics("../Figures/tree_plot.png")
```
